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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Identifier8JMKD3MGPEW34M/47LSPMH
Repositorysid.inpe.br/sibgrapi/2022/09.22.19.28
Last Update2022:09.23.16.43.09 (UTC) jvictor@fumec.br
Metadata Repositorysid.inpe.br/sibgrapi/2022/09.22.19.28.04
Metadata Last Update2023:05.23.04.20.43 (UTC) administrator
DOI10.1109/SIBGRAPI55357.2022.9991745
Citation KeySoaresFaFaFaPaGo:2022:AuSpHe
TitleAutomated Sperm Head Morphology Classification with Deep Convolutional Neural Networks
Short TitleAutomated Sperm Head Morphology Classification
FormatOn-line
Year2022
Access Date2024, Apr. 28
Number of Files1
Size312 KiB
2. Context
Author1 Soares, Marco Antônio Calijorne
2 Falci, Daniel Henrique Mourão
3 Farnezi, Marco Flávio Alves
4 Farnezi, Hana Carolina Moreira
5 Parreiras, Fernando Silva
6 Gomide, João Victor Boechat
Affiliation1 FUMEC University
2 FUMEC University
3 FUMEC University
4 FUMEC University
5 FUMEC University
6 FUMEC University
e-Mail Addressjvictor@fumec.br
Conference NameConference on Graphics, Patterns and Images, 35 (SIBGRAPI)
Conference LocationNatal, RN
Date24-27 Oct. 2022
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2022-09-23 16:43:09 :: jvictor@fumec.br -> administrator :: 2022
2023-05-23 04:20:43 :: administrator -> :: 2022
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Keywordsinfertility
sperm head classification
human sperm morphology
medical image classification
convolutional neural networks
deep learning
AbstractBackground and Objective: The morphological analysis of sperm cells is considered a tool in human fertility prognosis. However, this process is manual, time-consuming and dependent on professional expertise. From a computational perspective, this is a challenging problem due to the high intercategory similarity between the objects of interest and the amount of data available. In this paper, we propose a Convolutional Neural Network model to automate morphology analysis of human sperm heads. Methods: We performed K-Fold cross-validation experiments over two publicly available datasets and assessed the performance of the proposed approach using Accuracy, Precision, Recall and F1-Score.We also compared the proposed model with well-known Convolutional architectures and previous approaches on the same task. Results: Experimental evaluation showed that our approach achieved a macro-averaged F1-score of 0.95 while our best model attained an accuracy of 97.7%. The error analysis revealed a balanced classifier over different sperm head classes. Conclusions: We proved that the proposed approach outperformed the previous state-of-the-art results on this task.
Arrangementurlib.net > SDLA > Fonds > SIBGRAPI 2022 > Automated Sperm Head Morphology Classification
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPEW34M/47LSPMH
zipped data URLhttp://urlib.net/zip/8JMKD3MGPEW34M/47LSPMH
Languageen
Target FileSIBIGRAPI_AutomatedSpermHeadMorphologyClassification_INPE.pdf
User Groupjvictor@fumec.br
Visibilityshown
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPEW34M/495MHJ8
Citing Item Listsid.inpe.br/sibgrapi/2023/05.19.12.10 6
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination documentstage edition editor electronicmailaddress group holdercode isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project publisher publisheraddress readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session sponsor subject tertiarymark type url versiontype volume


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